Integrating Approximate Bayesian Computation with Complex Agent-Based Models for Cancer Research

Andrea Sottoriva () and Simon Tavaré ()
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Andrea Sottoriva: University of Cambridge, CRUK Cambridge Research Institute, Li Ka Shing Centre, Department of Oncology
Simon Tavaré: University of Cambridge, CRUK Cambridge Research Institute, Li Ka Shing Centre, Department of Oncology and DAMTP

A chapter in Proceedings of COMPSTAT'2010, 2010, pp 57-66 from Springer

Abstract: Abstract Multi-scale agent-based models such as hybrid cellular automata and cellular Potts models are now being used to study mechanisms involved in cancer formation and progression, including cell proliferation, differentiation, migration, invasion and cell signaling. Due to their complexity, statistical inference for such models is a challenge. Here we show how approximate Bayesian computation can be exploited to provide a useful tool for inferring posterior distributions. We illustrate our approach in the context of a cellular Potts model for a human colon crypt, and show how molecular markers can be used to infer aspects of stem cell dynamics in the crypt.

Keywords: ABC; cellular Potts model; colon crypt dynamics; stem cell modeling (search for similar items in EconPapers)
Date: 2010
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-7908-2604-3_5

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DOI: 10.1007/978-3-7908-2604-3_5

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